E4ST Description

E4ST Description

For a brief introductory description of the Engineering, Economic, and Environmental Electricity Simulation Tool (E4ST), see the HOME page. This page provides some additional information about E4ST.

E4ST Optimization Problem

At the heart of E4ST is an optimization problem that represents the behavior of the system operators, electricity end-users, generators, and generation and DC transmission developers. It is a cost minimization problem that minimizes the sum of generator variable costs, generator fixed costs, end-user consumer surplus losses, and investment costs. This problem mimics the system operation decisions of system operators, while simultaneously optimizing generator investment and retirement. This corresponds to the situation in systems where there is minimal exercise of market power by generators and investors.

The optimization problem is structured as a set of optimal power flow sub-problems, one for each of a set of representative hours used to model the range of operating conditions encountered over the course of the planning horizon. The costs in each of the sub-problems are weighted by the relative frequency of the corresponding representative hour and include fuel costs, other variable operating & maintenance costs, and any emission fees or variable charges faced by each generator. The sub-problems are then linked together into one large optimization problem by generator capacity constraints and costs, which can be changed via the construction of new generators and/or the retirement of existing generators. These linking costs include annual fixed costs of existing generation, as well as investment costs of any generators built as part of the optimization. When using models with thousands of generators, thousands of nodes (buses), and investment and retirement, the use of a DC linear approximate representation of the transmission system makes the optimization problem solvable using current commercial solvers.

Distinctive Characteristics & Capabilities

Various policies can be represented well. For example, renewable portfolio standards and must-run contracts are generation constraints and can be represented as such. Cap-and-trade programs and unit and plant emission limits are emission constraints, and can be represented as such. Emission fees are additions to the variable costs of emitting generators in proportion to their emissions, and can be represented as such.

E4ST’s distinctive capabilities and characteristics relative to other models include the following:

  • Uniquely comprehensive benefit-cost analysis capabilities.
  • Unique adaptability, transparency, and shareable nature.
  • Simultaneous optimization/prediction of operation and investment taking into account the details of the system, as in reality.
  • Realistic representation of power flows, employing Kirchoff’s laws of physics.
  • Compatibility with detailed grid models, for results that reflect the effects of transmission system constraints and topology: For example, the E4ST models of the four grids that cover most of the US and Canada contain the 19,000 existing generators with their detailed individual characteristics, tens of thousands of buildable generators, and approximately 20,000 high-voltage transmission line segments, including all of the high-voltage (>200 kV) segments as well as selected lower-voltage transmission segments.
  • Detailed, high-quality generator data: The models include the characteristics reported by each generator, including data reported to EIA in 2016 & 2017, and heat & emission rates reported to EPA in 2015. Missing values (e.g. emission rates of some very small units) are estimated.
  • Renewable energy detail: The model includes location- and hour-specific wind and solar availability for existing and buildable wind and solar generators.
  • Emission health effects model: The E4ST model of the US includes an air pollution transport and fate model, to enable E4ST to calculate county-by-county and total health effects of SO2, NOX, and fine particulate matter emissions.
  • Correct locations of SO2, NOX, and fine particulate emission sources relative to population and to transmission constraints, which strongly influences health outcomes and policy effects.
  • Annual (or seasonal) and hourly constraints for each hydropower facility, specifiable by the user.
  • Inclusion of Canada.
  • Prediction of storage operation, storage investment, and distributed energy adoption.
  • Prediction of the effects of uncertainty on investment and retirement (stochastic optimization).
  • Ability to include ramping constraints between consecutive hours or other time periods (e.g. 5-minute time periods).
  • Ability to integrate detailed geographic information systems (GIS) data
  • Suitability for integrating with models of the natural gas pipeline system.
  • Ability to be adapted to evaluate the effects of transmission and generation investments and retirements on the resilience of the electricity supply.
  • Ability to be adapted to simulate distribution systems as well.
  • Capability of running simulations in large batches to consider various futures or to perform tasks that require iteration.

The software is written in Julia using the JUMP.jl package to construct the optimization problem, allowing it to work with multiple state-of-the art free and commercial solvers. It is distributed as open-source under the standard GPL-3.0 License.

E4ST Team

E4ST has been developed by researchers at Resources for the Future (RFF), Cornell University, and Arizona State University (ASU), with funding, input, and review by USDOE, NSF, NYISO, and the Power Systems Engineering Research Center. Energy Visuals, Inc. has provided US and Canadian transmission system data. The team that currently manages E4ST is headed by Daniel Shawhan at RFF, with participation by Ray Zimmerman and Bill Schulze at Cornell and Daniel Tylavsky at ASU. They confer with colleagues at RFF, Cornell, and Citibank as necessary.

The E4ST México model construction team consists of José Carlos Fernández, Juan Carlos Belausteguigoitia, Daniel Shawhan, José Carlos Bardales, José Xavier Higueras, Paul Picciano, and Nicolás Suarez.

Past participants in the construction of the E4ST software or US & Canadian system models include Biao Mao, Carlos Murillo-Sánchez, Di Shi, John T. Taber, Robert J. Thomas, Yujia Zhu, Nan Li, Yingying Qi, Richard E. Schuler, Zamiyad Dar, and Andrew G. Kindle, Paul Picciano, Steven Witkin, Christoph Funke, Ethan Russell, and Sally Robson.